Update dataset card with paper, code, and robotics metadata

README.md

@@ -1,15 +1,42 @@
 ---
 license: cc-by-4.0
+task_categories:
+- robotics
 tags:
 - simulation
 - panda
-- isaacs
-- gym
+- isaac-gym
 - robot
 - manipulation
-- pushT
-- imitation
-- learning
+- push-t
+- imitation-learning
+- world-models
 ---
-Exper data (actions + frames) for several manipualation tasks: cube and push T (1, 2, 3 cubes / 1, 2, 3 T's).
-Panda robot from IsaacsGym simulator. Frames are 128x128, includes 2 camera views per-task.
+
+# Panda Dataset
+
+[**Project Page**](https://taldatech.github.io/lpwm-web) | [**Paper**](https://huggingface.co/papers/2603.04553) | [**GitHub**](https://github.com/taldatech/lpwm)
+
+This repository contains expert imitation trajectories (actions and frames) for several robotic manipulation tasks, as presented in the paper [Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling](https://huggingface.co/papers/2603.04553).
+
+## Dataset Description
+
+The data consists of expert trajectories generated using a **Panda robot** in the **IsaacGym** simulator. It was used to evaluate Latent Particle World Models (LPWM) in tasks requiring stochastic dynamics modeling and goal-conditioned imitation learning.
+
+- **Tasks**: 
+  - **Cube**: Manipulation of 1, 2, or 3 cubes.
+  - **Push-T**: Pushing T-shaped objects (1, 2, or 3 T's).
+- **Observations**: 128x128 resolution RGB frames, including 2 camera views per task.
+- **Actions**: Continuous robot actions corresponding to the trajectories.
+
+## Citation
+
+```bibtex
+@inproceedings{daniel2026latent,
+title={Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling},
+author={Tal Daniel and Carl Qi and Dan Haramati and Amir Zadeh and Chuan Li and Aviv Tamar and Deepak Pathak and David Held},
+booktitle={The Fourteenth International Conference on Learning Representations},
+year={2026},
+url={https://openreview.net/forum?id=lTaPtGiUUc}
+}
+```